Analysis of observed temperature and precipitation extremes over South Asia Jayashree Revadekar Centre for Climate Change Research Indian Institute of.

Analysis of observed temperature and Analysis of observed temperature and precipitation extremes over South Asiaprecipitation extremes over South Asia

Jayashree RevadekarJayashree RevadekarCentre for Climate Change ResearchCentre for Climate Change Research

Indian Institute of Tropical MeteorologyIndian Institute of Tropical MeteorologyPashan, PUNEPashan, PUNE

Indices of extremes are computed to determineIndices of extremes are computed to determine

IntensityIntensityFrequencyFrequency

Spell DurationSpell DurationSeasonal LengthSeasonal LengthExtreme RangeExtreme Range

Using daily timeseries tmax,tmin, precipUsing daily timeseries tmax,tmin, precipUsing climdexUsing climdex

Initial analysis is based on 121 Indian stations for Initial analysis is based on 121 Indian stations for temperature and 146 for precipitationtemperature and 146 for precipitation

Alexander et al., (2006) in JGRTank et al., (2006) in JGR

197 South Asian stations197 South Asian stations

As a part of APN project on extremesPakistan, India, Bagladesh, Nepal, Srilanka

Role of altitude & latitude : Revadekar et al., Int J. Climatology (2013)

Regional Trends Analysis : Munir Sheikh et al, Int. J. Climatology (2013) under revision

Attempt is also made to seeAttempt is also made to see

Role of Nino Index on Temperature Extremes Role of Nino Index on Temperature Extremes (Revadekar et al., 2009, Int. J. Climatology)(Revadekar et al., 2009, Int. J. Climatology)

Change in one extreme leading to change in Change in one extreme leading to change in other extreme other extreme

Change in extreme of season leading to Change in extreme of season leading to change in extreme of another seasonchange in extreme of another season

Change in extreme at one place leading to Change in extreme at one place leading to change in extreme to another place change in extreme to another place

Attempt is also made to seeAttempt is also made to see

Ability of Models in simulating extremesAbility of Models in simulating extremes

Projection of Extremes Projection of Extremes

Reconstruction of past Extremes using Reconstruction of past Extremes using proxy dataproxy data

IMD gridded data setIMD gridded data set

Relationship between summer monsoon Precipitation extremes and kharif foodgrain production over India

With preethi (2012 & 2013) Int. J. Climatology

Analysis is also done for rabi foodgrain

PRECIS DATAPRECIS DATA

Work in progress with Work in progress with CORDEX South AsiaCORDEX South Asia

CORDEX ARABCORDEX ARAB

Background :Background : The fourth assessment report of the Intergovernmental Panel on The fourth assessment report of the Intergovernmental Panel on

Climate Change (IPCC 2007) has concluded that the global Climate Change (IPCC 2007) has concluded that the global mean surface temperatures have risen by 0.74 ± 0.18°C when mean surface temperatures have risen by 0.74 ± 0.18°C when estimated by a linear trend over the last 100 years (1906–2005). estimated by a linear trend over the last 100 years (1906–2005). The rate of warming over the recent 50 years is almost double The rate of warming over the recent 50 years is almost double of that over the last 100 years (IPCC 2007), which is largely of that over the last 100 years (IPCC 2007), which is largely attributed to anthropogenic influencesattributed to anthropogenic influences

Over India, the mean maximum as well as minimum Over India, the mean maximum as well as minimum temperatures have increased by about 0.2°C per temperatures have increased by about 0.2°C per decade during the period 1971–2003, for the country decade during the period 1971–2003, for the country as a whole (Kothawale and Rupa Kumar 2005).as a whole (Kothawale and Rupa Kumar 2005).

Trend in maximum and Trend in maximum and minimum minimum temperature over temperature over North and South of North and South of 20N over India 20N over India

Recent study of Recent study of Kothawale et Kothawale et al.,2012), TACal.,2012), TAC

Need of Analysis on ExtremesNeed of Analysis on Extremes

Detection of change in climate against its Detection of change in climate against its variability is a key issue in climate research. variability is a key issue in climate research. Climate change is often expressed simply in Climate change is often expressed simply in terms of changes in mean climate. terms of changes in mean climate.

Average conditions may not show appreciable Average conditions may not show appreciable change but may be characterized by a variety of change but may be characterized by a variety of extreme situations. extreme situations.

Extremes could have more significant socio-Extremes could have more significant socio-economic consequences than the changes in economic consequences than the changes in meanmean

IPCC, 2001

Extremes are an expression of the variability, therefore the nature of Extremes are an expression of the variability, therefore the nature of

variability at different spatial and temporal scales is vital to our variability at different spatial and temporal scales is vital to our

understanding of extremes.understanding of extremes.

Expert Team on Climate Change Detection and Expert Team on Climate Change Detection and Indices (ETCCDI) coordinated the development Indices (ETCCDI) coordinated the development of a suite of climate change indices which of a suite of climate change indices which primarily focus on extremes. In all, 27 indices primarily focus on extremes. In all, 27 indices were defined which have been widely used for were defined which have been widely used for global and regional analyses of climate extremes. global and regional analyses of climate extremes. Present study is mainly based on same indices Present study is mainly based on same indices which are described at the link which are described at the link http://cccma.seos.uvic.ca/ETCCDMI/ . .

Network of stations for Global Analysis of 2001

Network of stations for Global Analysis of 2006

INDICES OF TEMPERATURE EXTREMESINDICES OF TEMPERATURE EXTREMES

FREQUENCY and SPELL DURATION INDICES :FREQUENCY and SPELL DURATION INDICES :

HOT EVENTSHOT EVENTS

Number of Hot days (Tx > user defined threshold)Number of Hot days (Tx > user defined threshold)

Number of Hot nights (Tn > user defined threshold)Number of Hot nights (Tn > user defined threshold)

Number of Hot days (Tx > 90Number of Hot days (Tx > 90thth Percentile of Tx) Percentile of Tx)

Number of Hot nights (Tn > 90Number of Hot nights (Tn > 90thth percentile of Tn) percentile of Tn)

Warm spell duration based on 90Warm spell duration based on 90thth percentile percentile

COLD EVENTSCOLD EVENTS

Number of Cold days (Tx < user defined threshold)Number of Cold days (Tx < user defined threshold)

Number of Cold nights (Tn < user defined threshold)Number of Cold nights (Tn < user defined threshold)

Number of Cold days (Tx < 10Number of Cold days (Tx < 10thth Percentile of Tx) Percentile of Tx)

Number of Cold nights (Tn < 10Number of Cold nights (Tn < 10thth percentile of Tn) percentile of Tn)

Cold spell duration based on 10Cold spell duration based on 10thth percentile percentile

INDICES OF TEMPERATURE EXTREMESINDICES OF TEMPERATURE EXTREMES

INTENSITY INDICES :INTENSITY INDICES :

Hottest day temperature Hottest day temperature

Hottest night temperatureHottest night temperature

Coldest day temperatureColdest day temperature

Coldest night temperatureColdest night temperature

Diurnal temperature rangeDiurnal temperature range

Range of Extreme : Hottest day minus coldest nightRange of Extreme : Hottest day minus coldest night

Growing Season LengthGrowing Season Length

The growing season is defined as starting when the temperature on five consecutive The growing season is defined as starting when the temperature on five consecutive days exceeds 5 °C, and ends after five consecutive days of temperatures below 5 days exceeds 5 °C, and ends after five consecutive days of temperatures below 5 °C.°C.

INDICES OF PRECIPITATION EXTREMESINDICES OF PRECIPITATION EXTREMES

FREQUENCY INDICES :FREQUENCY INDICES :

Number of days with RF > 10mmNumber of days with RF > 10mmNumber of days with RF > 20mmNumber of days with RF > 20mmNumber of days with RF > 30mmNumber of days with RF > 30mm

INTENSITY INDICES :INTENSITY INDICES :

One-day Maximum PrecipitationOne-day Maximum PrecipitationFive-day Maximum PrecipitationFive-day Maximum PrecipitationDaily Intensity (rainfall per rainydays)Daily Intensity (rainfall per rainydays)

INDICES OF PRECIPITATION EXTREMESINDICES OF PRECIPITATION EXTREMES

Rainfall due to Heavy Rain events based on 95Rainfall due to Heavy Rain events based on 95thth percentile percentile

Rainfall due to Very Heavy Rain events based on 99Rainfall due to Very Heavy Rain events based on 99thth percentile percentile

Continuous Dry DaysContinuous Dry Days

Continuous Wet DaysContinuous Wet Days

Extreme of a one place can be normal event of another placeExtreme of a one place can be normal event of another place

Basic analysis is done at station level/grid level.Basic analysis is done at station level/grid level.

Applied preliminary quality checks on each stationApplied preliminary quality checks on each station

Used Well distributed station dataUsed Well distributed station data

Once indices of extremes are computed for each station/grid further analysis is done to see changes

Trend AnalysisPDF Analysis mean

Epochal meanAnnual Cycle

Regional meansetc

For Extreme Analysis on South Asian Region :

Role of altitude and latitude on changes in extremes over South Asia during 1971 – 2000Revadekar et al., Int. J. Climatology, 33, 2013

Using 197 stations in Bangladesh, India, Nepal, Pakistan and Srilanka

FREQUENCY OF COLD EVENTS

FREQUENCY OF HOT EVENTS

INTENSITY

SPELL DURATION

IMPACT OF LATITUDE

COLD EVENTS

HOT EVENTS

Sign of trends in warm nights at stations with elevation > 500 m. Only trends with absolute value greater than 1.5 (%days/year) are shown. Circles represent negative trends and stars represent positive trends.

Mean Trends for categorized elevation

Average trends are computed for a categorized elevation rank for four different categories:

(1) <500 m; (2) 500–1000 m; (3) 1000–1500 m; and(4) >1500 m.

Higher magnitude trends over high altitude are seen through TX10p, TX90p, WSDI, TXx

Precipitation Extremes using Aphrodite

Computed indices of precipitation Extremes at each grid (0.5 x 0.5) using daily precipitation data for 1951 onwards for

JF

MAM

JJAS

OND

Annual

Annual Precipitation ExtremesClimatology R10mm

Annual Precipitation ExtremesTrends R10mm

Impact of System on R10mm

NormalSystem

Difference

RCM : PRECIS (Providing REgional Climates for Impacts Studies) developed by the Hadley Centre for Climate Prediction and Research, is applied for India to develop high-resolution climate change scenarios.

The model has ~50 km resolution

Simulations using PRECIS have been performed to generate the climate for present (1961-1990) and a future period (2071-2100) for two different socio-economic scenarios both characterized by regionally focused development but with priority to economic issues in one (A2 scenario) and to environmental issues in the other (B2 scenario).

The model simulations are performed with and without including sulfur cycle, to understand the role of regional patterns of sulfate aerosols in climate change.

PRECIS Simulations of Future ClimateMean Annual Cycles of All-India Rainfall and Temperature

Coldest Night Temperature

Both A2 and B2 scenarios show Both A2 and B2 scenarios show similar patterns of projected similar patterns of projected changes in the mean climate and changes in the mean climate and extremes towards the end of 21extremes towards the end of 21stst century. However, B2 scenario century. However, B2 scenario shows slight lower magnitudes of shows slight lower magnitudes of the projected changes than that the projected changes than that of A2 scenarios. of A2 scenarios.

Similar features are seen other Similar features are seen other intensity indices alsointensity indices also

Highest Maximum Temperature Highest Maximum Temperature Seasonal changes in a2 scenarios : wide spread warmingSeasonal changes in a2 scenarios : wide spread warming

Daily precipitation in a calendar year Daily precipitation in a calendar year for base line, a2 and changefor base line, a2 and change

One-day maximum precipitation One-day maximum precipitation for base line, a2, b2 and changefor base line, a2, b2 and change

Both a2 and b2 show Both a2 and b2 show similarity in changes;similarity in changes;

A2 is higher than b2A2 is higher than b2

Changes during Changes during summer monsoon are summer monsoon are higherhigher

International Conference on "Celebrating the Monsoon", 24-28 July 2007, Bangalore,

India

44

Scenarios for one-day and 5-day maximum. precipitation

Temperature and Precipitation Extremes

using CORDEXdaily maximum and minimum

temperature

Validation of models for categorized elevation

Similar to obsevational analysis Average trends are also computed for a categorized elevation rank for four different categories:

(1) <500 m; (2) 500–1000 m; (3) 1000–1500 m; and(4) >1500 m.

It is seen that models are able to capture elevation dependency in temperature extremes in addition to their spatial distribution

Trend analysis for Freezing nights RCP 85

Seasonal Length (Number of days between tmean > 5C to tmean < 5C) Mean Seasonal length in RCP26 and RCP85 (TOP Panel)Incremental Changes in RCP26 and RCP85 w.r.t. Historical (bottom Panel)

Trend analysis for warm spell duration RCP 85

Trend analysis for Diurnal Temperature Range : RCP 85

Trends in Maximum and Minimum Temperature for South Asia as a whole

Mean Trends for categorized elevation

Average trends are computed for a categorized elevation rank for four different categories:

(1) <500 m; (2) 500–1000 m; (3) 1000–1500 m; and(4) >1500 m.

Higher magnitude trends over high altitude i

SUMMARYSUMMARY

Good skill in depicting the surface climate over the Good skill in depicting the surface climate over the Indian region, particularly the orographic patterns of Indian region, particularly the orographic patterns of precipitation and temperature extremes. precipitation and temperature extremes.

Annual cycles of both precipitation and temperature Annual cycles of both precipitation and temperature extremes are well capturedextremes are well captured

Cold biases while simulating cold events. Cold biases while simulating cold events.

SScenarios of extremes:-cenarios of extremes:-

Model indicate marked increase in both rainfall and Model indicate marked increase in both rainfall and temperature towards the end of 21temperature towards the end of 21stst century century

Simulations under both RCP 4.5 and 8.5 scenarios indicate ...........>Simulations under both RCP 4.5 and 8.5 scenarios indicate ...........>

Increase in hot events Increase in hot events

Decrease in cold eventsDecrease in cold events

Enhancement in intensity.Enhancement in intensity.

The changes in temperature extreme in winter season are prominent than the rest The changes in temperature extreme in winter season are prominent than the rest of year.of year.

Both scenarios show similar patterns of projected changes in the mean climate Both scenarios show similar patterns of projected changes in the mean climate and extremes towards the end of 21and extremes towards the end of 21stst century. century.

4.5 scenario shows slight lower magnitudes of the projected changes than that of 4.5 scenario shows slight lower magnitudes of the projected changes than that of 8.5 scenarios.8.5 scenarios.

Elevation Dependency in changes in extremes is captured by modelsElevation Dependency in changes in extremes is captured by models

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